Automatic Detection of Volcanic Surface Deformation Using Deep Learning
Automatic Detection of Volcanic Surface Deformation Using Deep Learning
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基于深度学习的火山地表形变自动检测
DOI：
10.1029/2020jb019840
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
Jian Sun;C. Wauthier;K. Stephens;M. Gervais;G. Cervone;P. L. La Femina;M. Higgins
中科院分区：
文献类型：
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作者：
Jian Sun;C. Wauthier;K. Stephens;M. Gervais;G. Cervone;P. L. La Femina;M. Higgins
干涉合成孔径雷达(InSAR)提供地表位移的亚厘米测量，这是描述和监测火山区岩浆过程的关键。合成孔径雷达卫星经常获得的多时相干涉合成孔径雷达数据中对地表位移的大量测量有助于在全球范围内进行近乎实时的火山监测。然而，干涉图中大气信号的存在使InSAR测量结果的解释变得复杂，这甚至可能导致对InSAR信号的错误解释和火山动荡。鉴于现有的大量合成孔径雷达数据，需要一种自动的干涉合成孔径雷达数据处理和去噪方法，将令人关切的火山信号与大气信号和噪声分开。在这项研究中，我们采用深度学习策略，直接从通过InSAR时间序列方法获得的时间连续展开的表面位移中去除大气和其他噪声信号，该方法使用端到端卷积神经网络(CNN)，采用改进的U-Net编解码器结构。用模拟合成展开面位移图对CNN进行训练，然后将其应用于实际的InSAR数据。我们提出的体系结构能够检测火山表面位移的动态时空模式。我们发现，对于不同的形变率和信噪比(SNR)，建议采用集合平均策略来稳定检测结果。文中还给出了一个实例，将该方法应用于尼加拉瓜Masaya火山的InSAR数据，并用连续的GPS数据对结果进行了验证。实验结果证实，该网络确实能够有效地抑制大气等噪声，揭示无噪声的表面变形。
Interferometric Synthetic Aperture Radar (InSAR) provides subcentimetric measurements of surface displacements, which are key for characterizing and monitoring magmatic processes in volcanic regions. The abundant measurements of surface displacements in multitemporal InSAR data routinely acquired by SAR satellites can facilitate near real‐time volcano monitoring on a global basis. However, the presence of atmospheric signals in interferograms complicates the interpretation of those InSAR measurements, which can even lead to a misinterpretation of InSAR signals and volcanic unrest. Given the vast quantities of SAR data available, an automatic InSAR data processing and denoising approach is required to separate volcanic signals that are cause of concern from atmospheric signals and noise. In this study, we employ a deep learning strategy that directly removes atmospheric and other noise signals from time‐consecutive unwrapped surface displacements obtained through an InSAR time series approach using an end‐to‐end convolutional neural network (CNN) with an encoder‐decoder architecture, modified U‐net. The CNN is trained with simulated synthetic unwrapped surface displacement maps and is then applied to real InSAR data. Our proposed architecture is capable of detecting dynamic spatio‐temporal patterns of volcanic surface displacements. We find that an ensemble‐average strategy is recommended to stabilize detected results for varying deformation rates and signal‐to‐noise ratios (SNRs). A case study is also presented where this method is applied to InSAR data covering Masaya volcano, Nicaragua and the results are validated using continuous GPS data. The results confirm that our network can indeed efficiently suppress atmospheric and other noise to reveal the noise‐free surface deformation.